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        <title>TwiSe at SemEval-2017 Task 4: Five-point Twitter Sentiment Classification and Quantification</title>
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    <name type="personal">
        <namePart type="given">Georgios</namePart>
        <namePart type="family">Balikas</namePart>
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            <title>Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)</title>
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        <name type="personal">
            <namePart type="given">Steven</namePart>
            <namePart type="family">Bethard</namePart>
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        <name type="personal">
            <namePart type="given">Marine</namePart>
            <namePart type="family">Carpuat</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Marianna</namePart>
            <namePart type="family">Apidianaki</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Saif</namePart>
            <namePart type="given">M</namePart>
            <namePart type="family">Mohammad</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Daniel</namePart>
            <namePart type="family">Cer</namePart>
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        <name type="personal">
            <namePart type="given">David</namePart>
            <namePart type="family">Jurgens</namePart>
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            <publisher>Association for Computational Linguistics</publisher>
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    <abstract>The paper describes the participation of the team “TwiSE” in the SemEval-2017 challenge. Specifically, I participated at Task 4 entitled “Sentiment Analysis in Twitter” for which I implemented systems for five-point tweet classification (Subtask C) and five-point tweet quantification (Subtask E) for English tweets. In the feature extraction steps the systems rely on the vector space model, morpho-syntactic analysis of the tweets and several sentiment lexicons. The classification step of Subtask C uses a Logistic Regression trained with the one-versus-rest approach. Another instance of Logistic Regression combined with the classify-and-count approach is trained for the quantification task of Subtask E. In the official leaderboard the system is ranked 5/15 in Subtask C and 2/12 in Subtask E.</abstract>
    <identifier type="citekey">balikas-2017-twise</identifier>
    <identifier type="doi">10.18653/v1/S17-2127</identifier>
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        <url>https://aclanthology.org/S17-2127</url>
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    <part>
        <date>2017-08</date>
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            <start>755</start>
            <end>759</end>
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%0 Conference Proceedings
%T TwiSe at SemEval-2017 Task 4: Five-point Twitter Sentiment Classification and Quantification
%A Balikas, Georgios
%Y Bethard, Steven
%Y Carpuat, Marine
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%Y Cer, Daniel
%Y Jurgens, David
%S Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F balikas-2017-twise
%X The paper describes the participation of the team “TwiSE” in the SemEval-2017 challenge. Specifically, I participated at Task 4 entitled “Sentiment Analysis in Twitter” for which I implemented systems for five-point tweet classification (Subtask C) and five-point tweet quantification (Subtask E) for English tweets. In the feature extraction steps the systems rely on the vector space model, morpho-syntactic analysis of the tweets and several sentiment lexicons. The classification step of Subtask C uses a Logistic Regression trained with the one-versus-rest approach. Another instance of Logistic Regression combined with the classify-and-count approach is trained for the quantification task of Subtask E. In the official leaderboard the system is ranked 5/15 in Subtask C and 2/12 in Subtask E.
%R 10.18653/v1/S17-2127
%U https://aclanthology.org/S17-2127
%U https://doi.org/10.18653/v1/S17-2127
%P 755-759

Markdown (Informal)

[TwiSe at SemEval-2017 Task 4: Five-point Twitter Sentiment Classification and Quantification](https://aclanthology.org/S17-2127) (Balikas, SemEval 2017)

• TwiSe at SemEval-2017 Task 4: Five-point Twitter Sentiment Classification and Quantification (Balikas, SemEval 2017)

ACL

• Georgios Balikas. 2017. TwiSe at SemEval-2017 Task 4: Five-point Twitter Sentiment Classification and Quantification. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 755–759, Vancouver, Canada. Association for Computational Linguistics.